Three important results concerning the shape and the trends of the human mortality rate were discussed recently in demographic and epidemiological literature. These are the deceleration of the ...mortality rate at old ages, the tendency to rectangularization of the survival curve, and the decline of the old age mortality observed in the second part of the 20th century. In this paper we show that all these results can be explained by using a model with a new type of heterogeneity associated with individual differences in adaptive capacity. We first illustrate the idea of such a model by considering survival in a mixture of two subpopulations of individuals (called "labile" and "stable"). These subpopulations are characterized by different Gompertz mortality patterns, such that their mortality rates cross over. The survival chances of individuals in these subpopulations have different sensitivities to changes in environmental conditions. Then we develop a more comprehensive model in which the mortality rate is related to the adaptive capacity of an organism. We show that the trends in survival patterns experienced by a mixture of such individuals resemble those obtained in an analysis of empirical data on survival in developed countries. Lastly, we present evidence of the existence of subpopulations of phenotypes in both humans and experimental organisms, which were used as prototypes in our models. The existence of such phenotypes provides the possibility that at least part of today's centenarians originated from an initially frail part of the cohort.
Earlier studies have found large and increasing with time differences in mortality by education and marital status in post-Soviet countries. Their results are based on independent tabulations of ...population and deaths counts (unlinked data). The present study provides the first census-linked estimates of group-specific mortality and the first comparison between census-linked and unlinked mortality estimates for a post-Soviet country. The study is based on a data set linking 140,000 deaths occurring in 2001–2004 in Lithuania with the population census of 2001. The same socio-demographic information about the deceased is available from both the census and death records. Cross-tabulations and Poisson regressions are used to compare linked and unlinked data. Linked and unlinked estimates of life expectancies and mortality rate ratios are calculated with standard life table techniques and Poisson regressions. For the two socio-demographic variables under study, the values from the death records partly differ from those from the census records. The deviations are especially significant for education, with 72–73%, 66–67%, and 82–84% matching for higher education, secondary education, and lower education, respectively. For marital status, deviations are less frequent. For education and marital status, unlinked estimates tend to overstate mortality in disadvantaged groups and they understate mortality in advantaged groups. The differences in inter-group life expectancy and the mortality rate ratios thus are significantly overestimated in the unlinked data. Socio-demographic differences in mortality previously observed in Lithuania and possibly other post-Soviet countries are overestimated. The growth in inequalities over the 1990s is real but might be overstated. The results of this study confirm the existence of large and widening health inequalities but call for better data.
The Russian population continues to face political and economic challenges, has experienced poor general health and high mortality for decades, and has exhibited widening health disparities. The ...physiological factors underlying links between health and socioeconomic position in the Russian population are therefore an important topic to investigate. We used data from a population-based survey of Moscow residents aged 55 and older (n = 1495), fielded between December 2006 and June 2009, to address two questions. First, are social disparities evident across different clusters of biomarkers? Second, does biological risk mediate the link between socioeconomic status and health?
Health outcomes included subscales for general health, physical function, and bodily pain. Socioeconomic status was represented by education and an index of material resources. Biological risk was measured by 20 biomarkers including cardiovascular, inflammatory, and neuroendocrine markers as well as heart rate parameters from 24-h ECG monitoring.
For both sexes, the age-adjusted educational disparity in standard cardiovascular risk factors was substantial (men: standardized β = −0.16, 95% CI = −0.23 to −0.09; women: β = −0.25, CI = −0.32 to −0.18). Education differences in inflammation were also evident in both men (β = −0.17, CI = −0.25 to −0.09) and women (β = −0.09, CI = −0.17 to −0.01). Heart rate parameters differed by education only in men (β = −0.10, CI = −0.18 to −0.02). The associations between material resources and biological risk scores were generally weaker than those for education. Social disparities in neuroendocrine markers were negligible for men and women.
In terms of mediating effects, biological risk accounted for more of the education gap in general health and physical function (19–36%) than in bodily pain (12–18%). Inclusion of inflammatory markers and heart rate parameters—which were important predictors of health outcomes—may explain how we accounted for more of the social disparities than previous studies.
► Older Muscovites of both sexes exhibited substantial educational disparities in standard cardiovascular risk factors. ► Both sexes had an educational gradient with inflammatory markers; men had an educational gradient with heart rate parameters. ► Overall, biomarkers accounted for 19–36% of the education gap in general health and physical function. ► This share is larger than that found in prior studies, perhaps owing to the inclusion of inflammatory and heart parameters. ► These two sets of markers appeared to be important predictors of health outcomes.
Background: Socioeconomic differences in old-age mortality have not been studied in Germany. This study fills in the gap, evaluating mortality and life expectancy differentials among retired German ...men aged 65+ in 2003. Methods: Mortality rates are calculated from the administrative database on all public pensions and deaths of pensioners in 2003. Relative mortality rates and life expectancies are estimated for population subgroups according to the quintiles of lifetime earnings, type of medical insurance, broad occupational group, and residence in eastern or western Germany. Results: Among pension income quintiles, mortality varies by 60% and life expectancy at age 65 ranges from 14.9 to 18.5 years. Quintile-specific mortality and life-expectancy values are only slightly more favorable in western compared to eastern Germany. The mortality of manual workers is by 35% greater than that of salaried employees, while the mortality of those with mandatory public health insurance is 44% greater than the mortality of those with private or voluntary public health insurance. When all four characteristics are taken into account, relative mortality in the group with the highest mortality is three times higher than at the opposite end of the distribution, and corresponding life expectancies are 12.5 and 20 years. Half of all male deaths at ages 65+ are attributable to this variation. The mortality differentials remain significant at ages 80+. Conclusions: Socioeconomic mortality differentials persist into old age. They are similar in both regions and their magnitude is much greater than the diminishing mortality gap between the two parts of the country.
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